A call detail records data mart: Data modeling and OLAP analysis

Author:

Camilovic Dragana1,Becejski-Vujaklija Dragana2,Gospic Natasa3

Affiliation:

1. Faculty of Entrepreneurial Management, Novi Sad

2. Faculty of Organizational Sciences, Belgrade

3. Faculty of Transport and Traffic Engineering, Belgrade

Abstract

In order to succeed in the market, telecommunications companies are not competing solely on price. They have to expand their services based on their knowledge of customers' needs gained through the use of call detail records (CDR) and customer demographics. All the data should be stored together in the CDR data mart. The paper covers the topic of its design and development in detail and especially focuses on the conceptual/logical/physical trilogy. Some other design problems are also discussed. An important area is the problem involving time. This is why the implication of time in data warehousing is carefully considered. The CDR data mart provides the platform for Online Analytical Processing (OLAP) analysis. As it is presented in this paper, an OLAP system can help the telecommunications company to get better insight into its customers' behavior and improve its marketing campaigns and pricing strategies.

Publisher

National Library of Serbia

Subject

General Computer Science

Reference1 articles.

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1. Improve the performance of multidimensional data for OLAP by using an optimization approach;INTERNATIONAL CONFERENCE ON ADVANCES IN COMMUNICATION TECHNOLOGY AND COMPUTER ENGINEERING;2023

2. A Grey Wolf Optimization (GWO) based Cube Selection in OLAP Data Model;2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT);2022-02-16

3. A Data Warehouse Approach for Business Intelligence;2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE);2019-06

4. Land Use detection with cell phone data using topic models: Case Santiago, Chile;Computers, Environment and Urban Systems;2017-01

5. Academic data warehouse design using a hybrid methodology;Computer Science and Information Systems;2015

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